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06/08/2023
Differentially Private Image Classification by Learning Priors from Random Processes
In privacy-preserving machine learning, differentially private stochasti...
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01/30/2023
Extracting Training Data from Diffusion Models
Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion ha...
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09/15/2022
A Light Recipe to Train Robust Vision Transformers
In this paper, we ask whether Vision Transformers (ViTs) can serve as an...
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06/20/2022
Understanding Robust Learning through the Lens of Representation Similarities
Representation learning, i.e. the generation of representations useful f...
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03/31/2022
Generating High Fidelity Data from Low-density Regions using Diffusion Models
Our work focuses on addressing sample deficiency from low-density region...
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04/19/2021
Improving Adversarial Robustness Using Proxy Distributions
We focus on the use of proxy distributions, i.e., approximations of the ...
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04/16/2021
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries
Understanding the fundamental limits of robust supervised learning has e...
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03/22/2021
SSD: A Unified Framework for Self-Supervised Outlier Detection
We ask the following question: what training information is required to ...
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10/19/2020
RobustBench: a standardized adversarial robustness benchmark
Evaluation of adversarial robustness is often error-prone leading to ove...
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07/26/2020
Fast-Convergent Federated Learning
Federated learning has emerged recently as a promising solution for dist...
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07/08/2020
A Critical Evaluation of Open-World Machine Learning
Open-world machine learning (ML) combines closed-world models trained on...
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06/24/2020
Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks
With increasing expressive power, deep neural networks have significantl...
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05/17/2020
PatchGuard: Provable Defense against Adversarial Patches Using Masks on Small Receptive Fields
Localized adversarial patches aim to induce misclassification in machine...
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02/24/2020
On Pruning Adversarially Robust Neural Networks
In safety-critical but computationally resource-constrained applications...
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06/14/2019
Towards Compact and Robust Deep Neural Networks
Deep neural networks have achieved impressive performance in many applic...
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05/05/2019